Examples
Practical examples demonstrating the Dynex SDK across different problem domains. From simple BQM sampling to production-grade quantum machine learning, these examples cover the full spectrum of what Dynex can compute.Prerequisites
Basic Examples
Getting started with the fundamental SDK workflow:Simple BQM Sampling
Build and sample a Binary Quadratic Model on CPU and QPU
BQM Usage
Constructing BQMs with dimod, PyQUBO, and named variables
Algorithm Examples
Classic quantum algorithms implemented on the Dynex platform:Grover's Algorithm
Integer factorization via quantum amplitude amplification
Shor's Algorithm
Period-finding for efficient integer factorization
Optimization Algorithms
MaxCut, graph partitioning, job sequencing, and more
Machine Learning Examples
Quantum-enhanced ML algorithms with PyTorch and scikit-learn integration:ML Overview
QSVM, QPCA, QNN, QBM, and feature selection
QSVM
Quantum Support Vector Machine
QRBM / QBM
Quantum Restricted Boltzmann Machine
Neuromorphic Torch Layers
Hybrid quantum-classical PyTorch models
Industry Applications
Real-world applications across industries:| Domain | Examples |
|---|---|
| Finance | Portfolio optimization, collaborative filtering |
| Pharma / Health | Protein folding, RNA folding, molecule screening |
| Automotive | Traffic optimization, EV charging placement, CFD |
| Logistics | Aircraft loading, job sequencing, multi-vehicle routing |
| Aerospace | Satellite scheduling |
| Computer Vision | Image classification (Q-RBM), image super-resolution (Q-SISR) |